Azure Databricks in Ghana: How We Used it to Solve Big Data Challenges
Databricks is more than just a tool. It’s a paradigm, a way of thinking about data and how to use it. As a Data Scientist, you will be called upon to solve many different problems around data management. You need to be able to think outside the data silo, find new ways to access that data, and identify patterns that are hidden within your raw datasets. Then, you will have to figure out the best way to act on that information so as not to lose it in the future or spend too much time cleaning it up. Without an effective Data Science process and workflow, your company’s ability to quickly solve problems with data will be severely limited. This article highlights some of the challenges that Databricks solved for one Dataset Manager at Microsoft in Ghana who had provenance for their primary corporate databases and wanted a comprehensive solution for accessing all of their disparate sources of information from within Microsoft Azure. You will learn how we used Azure Databricks as part of our internal Data Science process and workflow in order to get results faster while minimizing risk in our Big Data environment..
What is Databricks?
Databricks is an open source, cloud-based data collaboration service. It enables teams to create, share, and manage interactive data experiences. It includes a data editor, data explorer, data modeler, and data storage features. It supports a range of third-party tools and is integrated with other cloud apps like Office 365. It’s a popular tool for organizations that want to move towards a DevOps data pipeline and create a unified data experience across different teams and applications. Like many of the other tools that are emerging in this space, Databricks is designed to help teams save time by cutting out the manual, tedious parts of working with data. In other words, it’s designed to be an efficient way to work with data.
The Data Science Process and workflow in Databricks
The first decision that you have to make is, “Which data source do I use to power my new model?” Your next question is, “What kind of analysis do I want to do?” Then, “How can I find meaningful insights?” Once you’ve answered these questions, you can start building your model. The first step is creating a data pipeline. The pipeline is where you decide which data sources to use, how you get the data into the database, and how you transform it for analysis. Next, you will create a model and select a data architecture. The data architecture specifies how data is organized in your model. The model is a representation of your data that’s used to store the data, filter and transform it, and find insights.
Microsoft Azure Databricks: Why use it?
Microsoft Azure Databricks is a fully managed service that enables enterprises to build and manage their cloud-based data strategy. It integrates with other Microsoft Azure services, as well as third-party apps, to deliver a unified data experience across teams and applications. With Azure Databricks, you can create a cloud-based data pipeline that automates the process of ingesting data from various sources, parsing it, transforming it, and storing it in a highly available and durable database. It also provides an intuitive monitoring and management portal so that you can easily view and manage your Databricks resources from any device.
Challenges in our Big Data Environment
The first challenge that our Dataset Manager faced was to understand that their current tools for managing the data, including the reporting and data visualization tools, were inadequate. They had tried to use the existing tools, but they were limited by the data silo. They knew that they needed a solution that would combine different data sources together and make it easier to visualize data. The second challenge was to understand how their current tools were negatively impacting their data quality. They were experiencing issues with missing data, incorrect data, duplicate data, and low throughput. Most of these issues could be traced back to the fact that they were using different tools to manage the data. They knew that they could solve these issues with a solution that combines different data sources together and makes it easier to visualize data.
The Solution: Our Dataset Manager’s workload in Databricks
Our Dataset Manager implemented a Databricks-based solution to enable them to have a comprehensive set of tools for managing the disparate data that they had. They could now use the data visualization and analytics tools that they were already using in their organization, and their data quality would improve dramatically because the data would be coming from a single source instead of different tools. The solution was to use Azure Data Lake Storage as a backend store for their data. The service provides fast ingest and low costs for storing large amounts of data while maintaining high availability and durability. The solution was to use the Apache Spark engine to analyze their data and create the data insights. The engine enables the use of large-scale data processing to get insights from large datasets.
Summary
As we can see, the world of data is growing faster than ever, and organizations need new ways to manage it effectively. This is why technologies such as Azure Databricks, Microsoft’s cloud-based data collaboration service, are so important. If you want to leverage new data sources and create new insights, you’ll need to use a data architecture that combines different kinds of data together. These tools are helping organizations to overcome some of their biggest data challenges, but they’re just scratching the surface. If you want to empower your team with data capabilities, you need to seek out tools and services that will help you discover new insights and make your organization’s data a critical asset.